confint_search: Confidence interval search procedure

In crctStepdown: Univariate Analysis of Cluster Trials with Multiple Outcomes

Confidence interval search procedure

Description

Search for the bound of a confidence interval using permutation test statistics

Usage

confint_search(
  start,
  b,
  n,
  nmodel,
  Xnull_,
  y,
  tr_,
  new_tr_mat,
  invS,
  family,
  family2,
  Z,
  type,
  nsteps = 1000L,
  weight = TRUE,
  alpha = 0.05,
  verbose = TRUE
)

Arguments

start	Numeric value indicating the starting value for the search procedure
b	Numeric value indicating the parameter estimate
n	Integer indicating the sample size
nmodel	Integer. The number of models
Xnull_	Numeric matrix. The covariate design matrix with the treatment variable removed
y	Numeric vector of response variables
tr_	Numeric vector. The original random allocation (0s and 1s)
new_tr_mat	A matrix. Each column is a new random treatment allocation with 1s (treatment group) and 0s (control group)
invS	A matrix. If using the weighted statistic then it should be the inverse covariance matrix of the observations
family	A statsfamily object
family2	A string naming the link function
Z	Matrix. Random effects design matrix describing cluster membership
type	String. Either "rw" for Romano-Wolf, "b" or "br" for bonferroni, "h" or "hr" for Holm, or "none"
nsteps	Integer specifying the number of steps of the search procedure
weight	Logical indicating whether to use the weighted (TRUE) or unweighted (FALSE) test statistic
alpha	The function generates (1-alpha)*100% confidence intervals. Default is 0.05.
verbose	Logical indicating whether to provide detailed output.

Value

The estimated confidence interval bound

crctStepdown documentation built on Sept. 14, 2023, 5:06 p.m.